Research on learning from demonstration offers a tremendous potential for future autonomous robots, but also for medical and clinical research. If we can start teaching machines by showing, our interaction with machines would become much more natural. If a machine can understand human movement, it can also be used in rehabilitation as a personal trainer that watches a patient and provides specific new excercises how to improve a diminished motor skill. Finally, the insights into biological motor control developed in learning from demonstration can help to build adaptive prosthetic devices that can be taught to improve the performance of a prosthesis.
In several projects, we have started to study learning from demonstration from a the view point of learning theory. Our working hypothesis is that a perceived movement is mapped onto a finite set of movement primitives that compete for perceived action. Such a process can be formulated in the framework of competitive learning. Each movement primitive predicts the outcome of a perceived movement and tries to adjust its parameters to achieve an even better prediction, until a winner is determined. In preliminary studies with anthropomorphic robots we have demonstrated the feasibility of our approaches. Nevertheless, many open problems remain for future research. Collaborators of our laboratory in Japan also try to develop theories on how the cerebellum could be involved in learning movement primitives. In our future research we will employ the humanoid robot above to study learning from demonstration in a man-humanoid environment.
Contact persons: Stefan Schaal
(:clmckeywordsearch Imitation Learning :)